peterwhycs / T Bear
Licence: apache-2.0
Detect EEG artifacts, outliers, or anomalies using supervised machine learning.
Programming Languages
Projects that are alternatives of or similar to T Bear
Curve
An Integrated Experimental Platform for time series data anomaly detection.
Stars: ✭ 408 (+6700%)
Mutual labels: anomaly-detection
Ganomaly
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Stars: ✭ 563 (+9283.33%)
Mutual labels: anomaly-detection
Ad examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
Stars: ✭ 641 (+10583.33%)
Mutual labels: anomaly-detection
Anomaly Detection Resources
Anomaly detection related books, papers, videos, and toolboxes
Stars: ✭ 5,306 (+88333.33%)
Mutual labels: anomaly-detection
Fieldtrip
The MATLAB toolbox for MEG, EEG and iEEG analysis
Stars: ✭ 481 (+7916.67%)
Mutual labels: eeg
Telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
Stars: ✭ 589 (+9716.67%)
Mutual labels: anomaly-detection
Credit Card Fraud Detection Using Autoencoders In Keras
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
Stars: ✭ 337 (+5516.67%)
Mutual labels: anomaly-detection
Loglizer
A log analysis toolkit for automated anomaly detection [ISSRE'16]
Stars: ✭ 785 (+12983.33%)
Mutual labels: anomaly-detection
Loghub
A large collection of system log datasets for AI-powered log analytics
Stars: ✭ 551 (+9083.33%)
Mutual labels: anomaly-detection
Logparser
A toolkit for automated log parsing [ICSE'19, TDSC'18, DSN'16]
Stars: ✭ 620 (+10233.33%)
Mutual labels: anomaly-detection
Eeglearn
A set of functions for supervised feature learning/classification of mental states from EEG based on "EEG images" idea.
Stars: ✭ 469 (+7716.67%)
Mutual labels: eeg
Midas
Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.
Stars: ✭ 591 (+9750%)
Mutual labels: anomaly-detection
Arl Eegmodels
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
Stars: ✭ 422 (+6933.33%)
Mutual labels: eeg
Rnn Time Series Anomaly Detection
RNN based Time-series Anomaly detector model implemented in Pytorch.
Stars: ✭ 718 (+11866.67%)
Mutual labels: anomaly-detection
Outlier Exposure
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
Stars: ✭ 343 (+5616.67%)
Mutual labels: anomaly-detection
Deep Learning For Hackers
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
Stars: ✭ 586 (+9666.67%)
Mutual labels: anomaly-detection
Datastream.io
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
Stars: ✭ 814 (+13466.67%)
Mutual labels: anomaly-detection
Getting Things Done With Pytorch
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.
Stars: ✭ 738 (+12200%)
Mutual labels: anomaly-detection
Adtk
A Python toolkit for rule-based/unsupervised anomaly detection in time series
Stars: ✭ 615 (+10150%)
Mutual labels: anomaly-detection
Time-Based EEG Artifact Rejection (T-BEAR)
Automated process for detecting and rejecting EEG artifacts.
Table of Contents
Description
This ongoing process explores supervised learning methods to detect artifacts in EEG data and possibly other time series.
Challenges:
- Current models are often task specific
- Feature engineering & selection
- High dimensionality
- High variability between subjects
- Low signal-to-noise ratio
- Non-stationary signal
Possible Prototypes:
-
Machine Learning
-
Supervised:
- Random Forest Classifier/Regressor
- Support Vector Classifier/Machine
-
Unsupervised:
- Isolation Forest
-
-
Deep Learning
- Supervised:
- Convolutional Neural Network (CNN)*
- Recurrent Neural Network (RNN)
- Supervised:
*CNN will be our goal for the final model.
Performance Metrics:
- F1 Score
- Precision
- Recall
- AUC-ROC Curve
Getting Started
Dependencies
-
Anaconda: within the
tbeardirectory containing the fileenvironment.ymlperform:- Problems may arise with Windows users.
conda env create -f environment.yml
- pip
pip install numpy scipy matplotlib pandas scikit-learn jupyter mne tensorflow
License
This project is licensed under the Apache License - see the LICENSE file for details
Acknowledgments
Inspiration, code snippets, etc.
- Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-811
- Roy, Yannick & Banville, Hubert & Albuquerque, Isabela & Gramfort, Alexandre & Faubert, Jocelyn. (2019). Deep learning-based electroencephalography analysis: a systematic review.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].